CRIStAL (SIGMA team), Lille
Date(s) : 27/11/2020 iCal
14 h 30 min - 15 h 30 min
Texture segmentation still constitutes an ongoing challenge, especially when processing large-size real world images. The aim of this work is twofold.
First, we provide a variational model for simultaneously extracting and regularizing local texture features, such as local regularity and local variance. For this purpose, a scale-free wavelet-based model, penalized by a Total Variation regularizer, is embeddedinto a convex optimisation framework. The resulting functional is shown to be strongly-convex, leading to a fast minimization scheme.
Second, we investigate Stein-like strategies for the selection of regularization parameters. A generalized Stein estimator of the quadratic risk is built. Then it is minimized via a quasi-Newton algorithm relying on a proposed generalized estimator of the gradientof the risk with respect to hyperparameters, leading to an automated and data-driven tuning of regularization parameters.
The overall procedure is illustrated on multiphasic flow images, analyzed as part of a long-term collaboration with physicists from the Laboratoire de Physique of ENS Lyon.